3D weak lensing with spin wavelets on the ball

Abstract

We construct the spin flaglet transform, a wavelet transform to analyze spin signals in three dimensions. Spin flaglets can probe signal content localized simultaneously in space and frequency and, moreover, are separable so that their angular and radial properties can be controlled independently. They are particularly suited to analyzing of cosmological observations such as the weak gravitational lensing of galaxies. Such observations have a unique 3D geometrical setting since they are natively made on the sky, have spin angular symmetries, and are extended in the radial direction by additional distance or redshift information. Flaglets are constructed in the harmonic space defined by the Fourier-Laguerre transform, previously defined for scalar functions and extended here to signals with spin symmetries. Thanks to various sampling theorems, both the Fourier-Laguerre and flaglet transforms are theoretically exact when applied to bandlimited signals. In other words, in numerical computations the only loss of information is due to the finite representation of floating point numbers. We develop a 3D framework relating the weak lensing power spectrum to covariances of flaglet coefficients. We suggest that the resulting novel flaglet weak lensing estimator offers a powerful alternative to common 2D and 3D approaches to accurately capture cosmological information. While standard weak lensing analyses focus on either real or harmonic space representations (i.e., correlation functions or Fourier-Bessel power spectra, respectively), a wavelet approach inherits the advantages of both techniques, where both complicated sky coverage and uncertainties associated with the physical modeling of small scales can be handled effectively. Our codes to compute the Fourier-Laguerre and flaglet transforms are made publicly available.

24 pages, 4 figures, version accepted for publication in PRD

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• The cross-correlation between 3d cosmic shear and the integrated Sachs-Wolfe effect
• 3D cosmic shear: numerical challenges, 3D lensing random fields generation and Minkowski Functionals for cosmological inference
• Science-driven 3D data compression
• Scattering Networks on the Sphere for Scalable and Rotationally Equivariant Spherical CNNs
• Needlet estimation of cross-correlation between CMB lensing maps and LSS
• Large-scale Modeling of the Observed Power Spectrum Multipoles
• Bayesian variational regularization on the ball